Agent skill

bio-filter-sequences

Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.

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npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-filter-sequences

SKILL.md

Version Compatibility

Reference examples tested with: BioPython 1.83+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Filter Sequences

"Filter sequences by length, quality, or content" → Apply boolean criteria to a stream of sequence records and write survivors to output.

  • Python: generator expression with SeqIO.parse() + SeqIO.write() (BioPython)
  • CLI: seqkit seq -m 200 (SeqKit) or awk on FASTA

Filter and select sequences based on various criteria using Biopython.

Required Imports

python
from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

Core Pattern

Use generator expressions for memory-efficient filtering:

python
records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if len(rec.seq) >= 100)
SeqIO.write(filtered, 'output.fasta', 'fasta')

Filter by Length

Minimum Length

python
records = SeqIO.parse('input.fasta', 'fasta')
long_seqs = (rec for rec in records if len(rec.seq) >= 500)
SeqIO.write(long_seqs, 'long.fasta', 'fasta')

Length Range

python
records = SeqIO.parse('input.fasta', 'fasta')
sized = (rec for rec in records if 100 <= len(rec.seq) <= 1000)
SeqIO.write(sized, 'sized.fasta', 'fasta')

Remove Short Sequences

python
min_length = 200
records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if len(rec.seq) >= min_length)
count = SeqIO.write(filtered, 'filtered.fasta', 'fasta')

Filter by ID

Select Specific IDs

python
wanted_ids = {'seq1', 'seq2', 'seq3'}
records = SeqIO.parse('input.fasta', 'fasta')
selected = (rec for rec in records if rec.id in wanted_ids)
SeqIO.write(selected, 'selected.fasta', 'fasta')

Select from ID File

Goal: Extract sequences whose IDs appear in an external list file.

Approach: Load IDs into a set for O(1) lookup, then stream-filter and write matches.

Reference (BioPython 1.83+):

python
with open('ids.txt') as f:
    wanted_ids = {line.strip() for line in f}

records = SeqIO.parse('input.fasta', 'fasta')
selected = (rec for rec in records if rec.id in wanted_ids)
SeqIO.write(selected, 'selected.fasta', 'fasta')

Exclude Specific IDs

python
exclude_ids = {'bad_seq1', 'bad_seq2'}
records = SeqIO.parse('input.fasta', 'fasta')
kept = (rec for rec in records if rec.id not in exclude_ids)
SeqIO.write(kept, 'kept.fasta', 'fasta')

Filter by ID Pattern

python
import re

pattern = re.compile(r'^chr\d+$')  # Match chr1, chr2, etc.
records = SeqIO.parse('input.fasta', 'fasta')
chromosomes = (rec for rec in records if pattern.match(rec.id))
SeqIO.write(chromosomes, 'chromosomes.fasta', 'fasta')

Filter by GC Content

python
from Bio.SeqUtils import gc_fraction

records = SeqIO.parse('input.fasta', 'fasta')
moderate_gc = (rec for rec in records if 0.4 <= gc_fraction(rec.seq) <= 0.6)
SeqIO.write(moderate_gc, 'moderate_gc.fasta', 'fasta')

High GC Sequences

python
high_gc = (rec for rec in records if gc_fraction(rec.seq) >= 0.6)

Low GC Sequences

python
low_gc = (rec for rec in records if gc_fraction(rec.seq) <= 0.4)

Filter by Sequence Content

Remove Sequences with N's

python
records = SeqIO.parse('input.fasta', 'fasta')
clean = (rec for rec in records if 'N' not in str(rec.seq).upper())
SeqIO.write(clean, 'clean.fasta', 'fasta')

Limit N Content

python
def n_fraction(seq):
    return str(seq).upper().count('N') / len(seq)

records = SeqIO.parse('input.fasta', 'fasta')
low_n = (rec for rec in records if n_fraction(rec.seq) < 0.05)

Contains Specific Motif

python
motif = 'GAATTC'  # EcoRI site
records = SeqIO.parse('input.fasta', 'fasta')
with_motif = (rec for rec in records if motif in str(rec.seq).upper())
SeqIO.write(with_motif, 'with_ecori.fasta', 'fasta')

Regex Pattern in Sequence

python
import re

pattern = re.compile(r'ATG.{30,100}T(AA|AG|GA)')  # ORF-like pattern
records = SeqIO.parse('input.fasta', 'fasta')
matches = (rec for rec in records if pattern.search(str(rec.seq)))

Filter by Description

Description Contains Keyword

python
records = SeqIO.parse('input.fasta', 'fasta')
kinases = (rec for rec in records if 'kinase' in rec.description.lower())
SeqIO.write(kinases, 'kinases.fasta', 'fasta')

Multiple Keywords (OR)

python
keywords = ['kinase', 'phosphatase', 'transferase']
records = SeqIO.parse('input.fasta', 'fasta')
enzymes = (rec for rec in records if any(k in rec.description.lower() for k in keywords))

Combine Multiple Filters

Goal: Remove sequences that fail any of several quality/content thresholds.

Approach: Define a predicate function that checks all criteria, apply it as a generator filter, and write survivors.

Reference (BioPython 1.83+):

python
from Bio.SeqUtils import gc_fraction

def passes_filters(record):
    if len(record.seq) < 100:
        return False
    if gc_fraction(record.seq) < 0.3 or gc_fraction(record.seq) > 0.7:
        return False
    if 'N' in str(record.seq).upper():
        return False
    return True

records = SeqIO.parse('input.fasta', 'fasta')
filtered = (rec for rec in records if passes_filters(rec))
SeqIO.write(filtered, 'filtered.fasta', 'fasta')

Sample Sequences

Random Sample (requires loading all)

python
import random

records = list(SeqIO.parse('input.fasta', 'fasta'))
sample = random.sample(records, min(100, len(records)))
SeqIO.write(sample, 'sample.fasta', 'fasta')

First N Sequences

python
from itertools import islice

records = SeqIO.parse('input.fasta', 'fasta')
first_100 = islice(records, 100)
SeqIO.write(first_100, 'first100.fasta', 'fasta')

Every Nth Sequence

python
records = SeqIO.parse('input.fasta', 'fasta')
every_10th = (rec for i, rec in enumerate(records) if i % 10 == 0)
SeqIO.write(every_10th, 'sampled.fasta', 'fasta')

Split by Criteria

Split by Length

Goal: Partition sequences into separate files based on a length threshold.

Approach: Load all records, apply list comprehension split, and write each partition.

Reference (BioPython 1.83+):

python
records = list(SeqIO.parse('input.fasta', 'fasta'))
short = [r for r in records if len(r.seq) < 500]
long = [r for r in records if len(r.seq) >= 500]
SeqIO.write(short, 'short.fasta', 'fasta')
SeqIO.write(long, 'long.fasta', 'fasta')

Common Errors

Error Cause Solution
Generator exhausted Used generator twice Re-create generator or use list()
Empty output Filter too strict Check filter conditions
Memory error List too large Use generator expressions

Related Skills

  • read-sequences - Parse sequences before filtering
  • write-sequences - Write filtered sequences to output
  • fastq-quality - Filter FASTQ by quality scores
  • paired-end-fastq - Synchronized filtering of paired reads
  • sequence-manipulation/motif-search - Filter by complex motif patterns
  • alignment-files - Filter aligned reads with samtools view -f/-F

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